Systems and methods for monitoring blood pressure

ABSTRACT

Various methods and systems for blood pressure monitoring are provided. A device for monitoring blood pressure may include a memory storing instructions for receiving one or more signals representative of one or more patient parameters, wherein at least one of the one or more signals comprises a plethysmography signal. The memory also stores instructions for determining a change in a pulse shape metric of the plethysmography signal and determining a change in a blood pressure signal over a period of time based on the one or more signals. The memory also stores instructions for determining a confidence level of the blood pressure signal based at least in part on a correlation between the change in the blood pressure signal and the change in the pulse shape metric over the period of time. The device also includes a processor configured to execute the instructions.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.13/974,292, filed Aug. 23, 2013, entitled “SYSTEMS AND METHODS FORMONITORING BLOOD PRESSURE”.

BACKGROUND

The present disclosure relates generally to medical devices, and, moreparticularly, to systems and methods for monitoring blood pressure of apatient.

This section is intended to introduce the reader to various aspects ofart that may be related to various aspects of the present disclosure,which are described and/or claimed below. This discussion is believed tobe helpful in providing the reader with background information tofacilitate a better understanding of the various aspects of the presentdisclosure. Accordingly, it should be understood that these statementsare to be read in this light, and not as admissions of prior art.

In the field of medicine, doctors often desire to monitor certainphysiological characteristics of their patients. In some cases,clinicians may wish to monitor a patient's blood pressure. Bloodpressure may be assessed using a wide variety of monitoring devices. Forexample, blood pressure may be monitored non-invasively via asphygmomanometer (e.g., a blood pressure cuff). In some circumstances,blood pressure may be continuously, non-invasively monitored usingmultiple pulse oximetry sensors located at multiple body sites on apatient and calculating a differential pulse transit time (DPTT).However, blood pressure signals obtained by a blood pressure cuff orbased on DPTT may be adversely affected by certain physiological events(e.g., changes in vasotone), and thus may not always accurately reflectthe patient's blood pressure. Additionally, such blood pressure signalsmay be subject to other sources of error, such as improper placement ofthe blood pressure monitoring device or errors in processing thereceived data. Therefore, systems and methods for monitoring a patient'sblood pressure and for determining a confidence level related to a bloodpressure signal are provided herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Advantages of the disclosed techniques may become apparent upon readingthe following detailed description and upon reference to the drawings inwhich:

FIG. 1 is a perspective view of a medical monitoring system inaccordance with an embodiment;

FIG. 2 is a block diagram of the medical monitoring system of FIG. 1, inaccordance with an embodiment;

FIG. 3A illustrates an example of a skew of a first derivative of aplethysmography signal and a pulse pressure signal over a period oftime;

FIG. 3B illustrates another example of a skew of a first derivative of aplethysmography signal and a pulse pressure signal over a period oftime;

FIG. 3C illustrates another example of a skew of a first derivative of aplethysmography signal and a pulse pressure signal over a period oftime;

FIG. 4 is a process flow diagram of a method of monitoring bloodpressure, in accordance with an embodiment;

FIG. 5 is a process flow diagram of a method of monitoring bloodpressure, in accordance with an embodiment;

FIG. 6 is a process flow diagram of an implementation of a method ofmonitoring blood pressure, in accordance with an embodiment;

FIG. 7 is a process flow diagram of a method of monitoring bloodpressure based on a differential pulse transit time, in accordance withan embodiment;

FIG. 8 is a process flow diagram of a method of monitoring bloodpressure based on a skew of a first derivative, in accordance with anembodiment; and

FIG. 9 is a process flow diagram illustrating a method for monitoringblood pressure of a patient using multiple different monitoringtechniques, in accordance with an embodiment.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

Provided herein are techniques for monitoring blood pressure and fordetermining a confidence level of the blood pressure signal. One or morespecific embodiments of the present techniques will be described below.In an effort to provide a concise description of these embodiments, notall features of an actual implementation are described in thespecification. It should be appreciated that in the development of anysuch actual implementation, as in any engineering or design project,numerous implementation-specific decisions must be made to achieve thedevelopers' specific goals, such as compliance with system-related andbusiness-related constraints, which may vary from one implementation toanother. Moreover, it should be appreciated that such a developmenteffort might be complex and time consuming, but would nevertheless be aroutine undertaking of design, fabrication, and manufacture for those ofordinary skill having the benefit of this disclosure.

A physician may monitor a patient's blood pressure through the use ofvarious blood pressure monitoring devices and systems. For example, apatient's blood pressure may be monitored via a blood pressure cuff. Insome cases, a patient's blood pressure may be derived by processing timedelays between two or more characteristic points within a singleplethysmography (PPG) signal obtained from a single pulse oximetrysensor. The characteristics points may be turning points of a derivativeof the PPG signal or turning points of the PPG signal, for example. Thetime delay may correspond to the time it takes the pulse wave to travela predetermined distance, such as a distance from the sensor to areflection point and back to the sensor. Various techniques for derivingblood pressure based on a comparison of time delays between certaincomponents of a single PPG signal obtained from a single sensor isdescribed in U.S. Publication No. 2009/0326386, entitled “Systems andMethods for Non-Invasive Blood Pressure Monitoring,” the entirety ofwhich is incorporated herein by reference.

In other cases, a patient's blood pressure may be continuously,non-invasively monitored via multiple pulse oximetry sensors placed atmultiple locations on the patient's body. As described in U.S. Pat. No.6,599,251, entitled “Continuous Non-invasive Blood Pressure MonitoringMethod and Apparatus” the entirety of which is incorporated herein byreference, multiple PPG signals may be obtained from the multiple pulseoximetry sensors, and the PPG signals may be compared against each otherto estimate the patient's blood pressure. More particularly, when thelocations of two sensors are at different distances from the patient'sheart or along different paths (e.g., at the finger and forehead), adifferential pulse transit time (DPTT) may be determined. The DPTT maythen be used to compute blood pressure on a continuous or periodic basis(e.g., at predetermined intervals). A DPTT may represent the differencein arrival times of a portion of a cardiac wave between the twolocations, and may be determined by comparing corresponding points inthe two PPG signals. However, the DPTT may be adversely affected byvarious physiological events, such as changes in a patient's arterialcompliance (e.g., vasotone). Additionally, the DPTT may be subject tovarious other sources of error, such as improper placement of thesensors or errors in processing the received data. Thus, the bloodpressure signal derived from DPTT may not accurately reflect thepatient's blood pressure. Blood pressure signals and measurementsobtained via other blood pressure monitoring devices or techniques mayalso be subject to various sources of error.

In view of the foregoing, it may be desirable to determine a confidencelevel related to a blood pressure signal. Accordingly, provided hereinare techniques for monitoring blood pressure and for determining aconfidence level (e.g., a confidence measure, confidence metric, signalquality metric, quality metric, etc.) of the blood pressure signal(e.g., blood pressure measurement). The confidence level may relate to,and may provide some indication of, the accuracy and/or reliability ofthe blood pressure signal. Additionally, the confidence level may beprovided to a caregiver or a user and/or certain actions may betriggered based on the determined confidence level. For example, thesystem may be configured to display a blood pressure signal and/or toprovide a confirmation that the blood pressure signal is reliable if ablood pressure signal is determined to have a high confidence level. Insome cases, the system may be configured to discard blood pressure dataand/or to provide an alert if a blood pressure signal is determined tohave a low confidence level. Such systems and methods may in turnprovide improved patient monitoring and patient care.

In certain embodiments, the confidence level of the blood pressuresignal may be determined based at least in part on a metric related tothe PPG signal. In some embodiments, the metric may be a pulse shapemetric. The pulse shape metric may be a center of area, a rotationalmoment, a mean, a median, a standard deviation, kurtosis, a pulse wavearea, peak to peak amplitude, a mean path ratio, notch positioning, anatural frequency, a damping factor, or a skew, for example. The metricsmay be determined with respect to the raw PPG signal or a derivative ofthe PPG signal (e.g., a first derivative, a second derivative, etc.). Incertain embodiments, the metrics may be determined with respect to awhole pulse or only with respect to an upstroke portion (e.g., systolic)portion of the pulse. Additionally, in certain embodiments, the metricsmay be determined with respect to an ensemble averaged derivativewaveform with each pulse normalized in amplitude and/or in time usingthe pulse period prior to ensemble averaging. Each of the metrics may beexpected to correlate with the blood pressure signal in a particularmanner, and the relationship between the blood pressure signal and thepulse shape metric over a period of time may be used to determine aconfidence level of the blood pressure signal. Additionally, anycombination of two or more different pulse shape metrics may be used todetermine a confidence level of the blood pressure signal. The presentapplication generally describes a skew of a first derivative of a PPGsignal (e.g., a skew metric) to facilitate explanation and to simplifydiscussion; however, as noted above, any suitable metric may be used inaccordance with the disclosed techniques.

The skew metric trends with pulse pressure, and changes in the skewmetric are expected to positively correlate with changes in bloodpressure. Additionally, the skew metric may not be affected by changesin compliance of the arterial system (e.g., may be independent ofchanges in vasotone or compliance). Thus, a relationship or correlationbetween the change in the skew metric and the change in the bloodpressure signal over a period of time may be utilized to determine theconfidence level of the blood pressure signal, even during periods ofchanges in arterial compliance, as discussed in more detail below.

Additionally, the disclosed embodiments may be used to monitor apatient's blood pressure based on one or more pulse shape metrics, suchas the skew of the PPG signal or the skew of the first derivative of thePPG signal. For example, the skew of the first derivative of the PPGsignal may be appropriately scaled to provide a blood pressure signal.Such a system may facilitate blood pressure monitoring with fewercomponents (e.g., a single PPG sensor) and lower overall cost, thusimproving the convenience of patient monitoring and lowering the cost ofpatient care.

With the foregoing in mind, FIG. 1 is a front perspective view of anembodiment of a medical monitoring system 10 that may be used formonitoring blood pressure and for determining a confidence level of theblood pressure signal. As shown, the system 10 includes a patientmonitor 12 that is coupled to multiple sensors 14. The sensors 14 may becoupled to the monitor 12 wirelessly or via a cable 16. In oneembodiment, the sensors 14 may be standard pulse oximetry sensors, andthe monitor 12 may be a pulse oximetry monitor. Each of the sensors 14may be configured to obtain a PPG signal, and the monitor 12 may beconfigured to process the PPG signals. As discussed in more detailbelow, in some embodiments, the system 10 may include only one sensor14, multiple sensors 14, and/or may include additional types of sensorsor monitoring devices (e.g., blood pressure monitoring devices).

Each sensor 14 may include one or more emitters 18 configured totransmit light. In addition, the sensor 14 may include one or moredetectors 20 to detect light transmitted from the emitters 18 into apatient's tissue after the light has passed through the blood perfusedtissue. The detectors 20 may generate a photoelectrical signalcorrelative to the amount of light detected. The emitter 18 and detector20 may be disposed in a sensor housing. The emitter 18 may be a lightemitting diode, a superluminescent light emitting diode, a laser diodeor a vertical cavity surface emitting laser (VCSEL). Generally, thelight passed through the tissue is selected to be of one or morewavelengths that are absorbed by the blood in an amount representativeof the amount of the blood constituent present in the blood. The amountof light passed through the tissue varies in accordance with thechanging amount of blood constituent and the related light absorption.For example, the light from the emitter 18 may be used to measure bloodpressure, blood oxygen saturation, water fractions, hematocrit, or otherphysiological parameters of the patient. In certain embodiments, theemitter 18 may emit at least two (e.g., red and infrared) wavelengths oflight. The red wavelength may be between about 600 nanometers (nm) andabout 700 nm, and the IR wavelength may be between about 800 nm andabout 1000 nm. However, any appropriate wavelength (e.g., green, yellow,etc.) and/or any number of wavelengths (e.g., three or more) may beused. It should be understood that, as used herein, the term “light” mayrefer to one or more of ultrasound, radio, microwave, millimeter wave,infrared, visible, ultraviolet, gamma ray or X-ray electromagneticradiation, and may also include any wavelength within the radio,microwave, infrared, visible, ultraviolet, or X-ray spectra, and thatany suitable wavelength of light may be appropriate for use with thepresent disclosure.

The monitor 12 may be configured to receive and to process signals fromone or more sensors 14. In certain embodiments, the monitor 12 may beconfigured to determine a patient's blood pressure based on the signalsreceived from the sensors 14. For example, two sensors 14 may bepositioned on the patient's body such the sensors 14 are located atdifference distances from the patient's heart or along different pathsfrom the patient's heart. The monitor 12 may be configured to determinethe DPTT by comparing the difference in arrival times of a portion of acardiac wave between the sensor locations, and the monitor 12 may beconfigured to derive the blood pressure signal based on the DPTT. Asdiscussed above, the patient's blood pressure may also be derived from asingle PPG signal obtained by a single sensor 14, in some embodiments.

Additionally or alternatively, the monitor 12 may be configured todetermine various metrics based on the PPG signal. For example, themonitor 12 may be configured to determine a pulse shape metric, such asa skew of a first derivative (e.g., skew metric) of the PPG signalreceived from at least one sensor 14. As explained in more detail below,the monitor 12 may be configured to determine a confidence level of ablood pressure signal based at least in part on the skew metric. Incertain embodiments, the monitor 12 may also be configured to determinevarious other physiological parameters, such as blood oxygen saturation,based on the signal received from the one or more sensors 14.

The monitor 12 may include a monitor display 24 configured to displayinformation regarding the physiological parameters, information aboutthe system, and/or alarm indications, for example. The monitor 12 mayalso include various input components 26, such as knobs, switches, keysand keypads, buttons, etc., to provide for operation and configurationof the monitor 12 and monitoring system 10. The monitor 12 may include awireless module for transmitting and receiving wireless data, a memory,a processor, and various monitoring and control features.

As discussed above, the monitor 12 may determine blood pressure, theskew metric, and/or various physiological parameters of the patient. Incertain embodiments the sensors 14, instead of or in addition to themonitor 12, may calculate the skew metric and/or various physiologicalparameters. In some embodiments, the monitor 12 may also be coupled to amulti-parameter monitor 30 via a cable 32 connected to a sensor inputport or via a cable 34 connected to a digital communication port. Inaddition to the monitor 12, or alternatively, the multi-parametermonitor 30 may be configured to determine blood pressure, the skewmetric, and/or various other physiological parameters. Themulti-parameter monitor 30 may be configured to provide a centraldisplay 36 for visualization of information from the monitor 12 and fromother monitoring devices or systems. The multi-parameter monitor 30 mayfacilitate presentation of patient data, such as blood pressure signalsand/or confidence levels determined by system 10 and/or physiologicalparameters determined by the monitor 12 or by other patient monitoringsystems (e.g., electrocardiographic (ECG) monitoring system, arespiration monitoring system, etc.). For example, the multi-parametermonitor 30 may display a graph of a blood pressure signal and acorresponding confidence level, a graph of SpO₂ values, a current pulserate, an electrocardiograph, and/or other related patient data in acentralized location for quick reference by a medical professional.Although cables 32 and 34 are illustrated, it should be understood thatthe monitor 12 may be in wireless communication with the multi-parametermonitor 30.

If in wireless communication, the various wirelesstransceivers/receivers of the various components of the system 10, maybe configured to communicate using the IEEE 802.15.4 standard, and maybe, for example, ZigBee, WirelessHART, or MiWi modules. Additionally oralternatively, the wireless modules may be configured to communicateusing the Bluetooth standard, one or more of the IEEE 802.11 standards,an ultra-wideband (UWB) standard, or a near-field communication (NFC)standard. As described further below, the PPG sensor 14 may wirelesslytransmit either raw detector signals or calculated physiologicalparameter values to the patient monitor 12. Additionally, the monitor 12may use the wireless module to send the sensor 14 instructions and/oroperational parameters set by a user or an operator using the monitor12.

The multiple sensors 14 illustrated in FIG. 1 may be utilized todetermine the DPTT for continuous non-invasive blood pressuremonitoring. However, it should be understood that the system 10 may haveonly a single sensor 14 and may be configured to determine the bloodpressure signal, the skew metric, and/or the confidence level using onlya single sensor 14. Thus, the blood pressure signal may be determinedfrom the PPG signal from the single sensor 14. For example, in someembodiments, the blood pressure measurements may be derived byprocessing time delays between characteristic points of a single PPGsignal obtained from the single sensor 14, and the skew metric may alsobe derived based on the single PPG signal obtained from the singlesensor 14. Alternatively, or in addition, it should be understood thatthe blood pressure signal may be obtained through any suitable bloodpressure monitoring device or techniques. In such systems, the bloodpressure monitoring device may obtain a blood pressure signal andprovide the blood pressure signal to the monitor 12 or to themulti-parameter monitor 30, for example. The blood pressure monitoringdevice may communicate the blood pressure signal to the monitor 12 or tothe multi-parameter monitor 30 wirelessly or via a cable, for example.The monitor 12 or multi-parameter monitor 30 may receive the bloodpressure signal and may determine a confidence level of the bloodpressure signal based at least in part on the skew metric. In suchsystems, only a single sensor 14 may be required to derive the skewmetric for determining the confidence level of the blood pressuresignal.

FIG. 2 is a block diagram of the system 10. As discussed above, thesensors 14 may include one or more emitters 18 capable of emitting oneor more wavelengths of light, and one or more detectors 20 capable ofdetecting light at various intensities and wavelengths. In suchembodiments, the data provided by the sensors 14 may be used tocalculate physiological parameters, such as blood pressure, blood oxygensaturation, and pulse rate. Additionally, the PPG signal may be used bythe monitor 12 to calculate the skew metric. The skew metric may in turnbe utilized to determine the confidence level of the blood pressuresignal, as described in more detail below. Again, although multiplesensors 14 are shown in FIG. 2, it should be understood that the secondsensor is optional, and only a single sensor 14 may be used in someembodiments.

In operation, light enters the detector 20 after propagating through thetissue of the patient. The detector 20 may convert the light at a givenintensity, which may be directly related to the absorbance and/orreflectance of light in the tissue of the patient, into an electricalsignal. Each sensor 14 may also include an encoder 42, which may containinformation about the sensor 14, such as what type of sensor it is(e.g., a type of sensor, a location where the sensor is to be placed,etc.) and how the sensor 14 is to be driven (e.g., wavelength of lightemitted by the emitter 18). This information may allow the monitor 12 toselect appropriate algorithms and/or calibration coefficients or toderive a filter for estimating the patient's physiologicalcharacteristics. The encoder 42 may, for instance, be a memory on whichinformation may be stored for communication to the monitor 12. Theencoder 42 may store information related to the wavelength of theemitters 18. The encoder 42 may, for instance, be a coded resistor,EEPROM or other coding devices (such as a capacitor, inductor, PROM,RFID, parallel resident currents, or a colorimetric indicator) that mayprovide a signal to a microprocessor 44 or other processing circuitry ofthe monitor 12 related to the characteristics of the sensor 14 to enablethe microprocessor 44 to determine the appropriate calibrationcharacteristics. In some embodiments, the data or signal from theencoder 42 may be decoded by a detector/decoder 46 in the monitor 12. Insome embodiments, the encoder 42 and/or the decoder 46 may not bepresent.

The microprocessor 44 of the monitor 12 may be coupled to an internalbus 48. The received signal from the sensor 14 may be passed through anamplifier 52, a low pass or bandpass filter 54, and an analog-to-digitalconverter 56. A time processing unit (TPU) 58 may provide timing controlsignals to light drive circuitry 60, which controls when the opticalcomponents of the optical sensor (e.g., sensor 14) is activated, and, ifmultiple light sources are used, the multiplexed timing for thedifferent light sources. TPU 58 may also control the gating-in ofsignals from sensor 14 or sensors 14 through a switching circuit 62.These signals are sampled at the proper time, depending at least in partupon which of multiple light sources is activated, if multiple lightsources are used. The digital data may then be stored in a queued serialmodule (QSM) 64, for later downloading to RAM 66 or ROM 68 as QSM 64fills up. In addition, the monitor 12 may include a display 24 andcontrol inputs 26, such as knobs, switches, keys and keypads,touchscreens, buttons, etc., to provide for operation and configurationof the monitor 12.

Based at least in part upon the received signals corresponding to thelight received by optical components of the sensors 14, themicroprocessor 44 may determine the skew metric, the blood pressure, theoxygen saturation, the heart rate, and/or other physiological parametersusing various algorithms. The algorithms may employ certaincoefficients, which may be empirically determined, and may correspond tothe wavelengths of light used. The algorithms and coefficients may bestored in a ROM 68 or other suitable computer-readable storage medium ormemory circuitry and accessed and operated according to microprocessor44 instructions. Additionally, the microprocessor 44 may determine theconfidence level of the blood pressure signal based on the skew metric.

As discussed above, one or more functions of the monitor 12 may also beimplemented directly in the sensors 14. For example, in someembodiments, the sensors 14 may include one or more processingcomponents configured to calculate the skew metric, the blood pressure,the confidence level, and/or various physiological parameters from thesignals obtained from the patient. The sensors 14 may have varyinglevels of processing power, and may wirelessly output data in variousstages to the monitor 12. For example, in some embodiments, the dataoutput to the monitor 12 may be analog signals, such as detected lightsignals (e.g., pulse oximetry signals or regional saturation signals),or processed data.

According to various embodiments, the system 10 may be utilized todetermine the confidence level of the blood pressure signal. Asindicated above, the confidence level may be based at least in part on apulse shape metric, such as a skew metric. FIGS. 3A-3C depicts examplesof the skew metric 72 and the pulse pressure 74 over a period of time.The skew metric 72 was determined from a PPG signal obtained from thepatient, as explained in more detail below. The pulse pressure 74 wasmeasured via a peripheral arterial line placed in an arm of the patient.Pulse pressure is the difference between systolic pressure and diastolicpressure, and is related to a patient's blood pressure. As shown inFIGS. 3A-3C, the skew metric generally trends with the pulse pressure.Thus, the skew metric is also expected to generally positively correlatewith (e.g., trend with) the blood pressure signal. With reference toFIG. 3A, an increase 76 in the skew metric 72 attends an increase 78 inthe pulse pressure 74. In the illustrated example, a drug (e.g., avasoactive drug) was administered to the patient just prior to theincrease 78 in pulse pressure 74. As shown, the skew metric 72 trendedwith the increasing pulse pressure 74 even after administration of thedrug (e.g., the skew metric 72 was not adversely affected byadministration of the drug or by changes in arterial compliance due tothe drug). Accordingly, a relationship or correlation between the skewmetric 72 and the blood pressure signal over a period of time can beused to determine the confidence level of the blood pressure signal,even in the presence of changes in arterial compliance and changes invasotone.

Skew metrics generally measure the asymmetry of a signal around a meanor average value, and thus generally characterize the degree ofasymmetry and the shape of such signals. The monitor 12 may beconfigured to derive and analyze the skew metric from the PPG signal viaany suitable method. For example, the monitor 12 may identify individualpulses and then derive and analyze the skew metric from each individualpulse. One or more digital IR and red waveforms may be bandpassed,normalized, and whitened to produce one or more filtered waveforms.These filtered waveforms may then be used to calculate the skew metric.In an embodiment, components of the skew metric may be calculated asfollows:

${skew} = {{\frac{n \cdot {\sum\limits_{t}\; \left( {x_{t} - \overset{\_}{x}} \right)^{3}}}{\left( {\left( {n - 1} \right)\left( {n - 2} \right)\left( \sigma^{3} \right)} \right)}\mspace{14mu} {where}\mspace{14mu} x_{t}} = \left( {{PPG}_{t} - {PPG}_{t - 1}} \right)}$

The skew metric may be monitored continuously over time and/or the skewmetric may be determined periodically for a particular period of time(e.g., 30 seconds, 1 minute, 2 minutes, etc.). In an embodiment, theskew metric is determined continuously as a moving average over a timewindow (e.g., 30 seconds, 1 minute, 2 minutes, etc.).

As discussed in more detail below, the skew metric can be utilized todetermine the confidence level of the blood pressure signal. The skewmetric is not adversely affected by changes in vasotone, and the skewmetric trends with the patient's blood pressure even during periods ofchange in vasotone. Thus, the skew metric is particularly well-suitedfor providing a validation and for determining a confidence level ofblood pressure signals, such as a blood pressure signal derived fromDPTT, derived from a single PPG waveform, or received from a bloodpressure monitoring device. For example, if the blood pressure signaldoes not trend with the skew metric, it may indicate that the bloodpressure signal is adversely affected by changes in vasotone or that themeasurement was subject to some other source of error. Various methodsfor monitoring blood pressure and for determining the confidence levelof the blood pressure signal are discussed in more detail below.

FIGS. 4-8 are flow charts illustrating various methods for monitoringblood pressure, in accordance with the present disclosure. The methodsinclude various steps represented by blocks. It should be noted any ofthe methods provided herein, may be performed as an automated procedureby a system, such as system 10. Although the flow charts illustrate thesteps in a certain sequence, it should be understood that the steps maybe performed in any suitable order and certain steps may be carried outsimultaneously, where appropriate. Further, certain steps or portions ofthe methods may be performed by separate devices. For example, a firstportion of the method may be performed by a blood pressure monitoringdevice, while a second portion of the method may be performed by thesensor 14 and/or monitor 12. In addition, insofar as steps of themethods disclosed herein are applied to the received signals, it shouldbe understood that the received signals may be raw signals or processedsignals. That is, the methods may be applied to an output of thereceived signals.

With reference to FIG. 4, a method for monitoring blood pressureaccording to an embodiment is generally indicated by reference number80. In certain embodiments, the method 80 begins with receiving a bloodpressure signal at step 82. The blood pressure signal may be receivedfrom any suitable blood pressure monitoring device. In certainembodiments, the blood pressure signal may be derived from one or morePPG signals obtained by one or more sensors 14. For example, the bloodpressure signal may be derived from the DPTT derived from the PPGsignals from two sensors 14. Alternatively, the blood pressure signalmay be derived from the PPG signal of a single sensor 14. At step 84, amonitoring device, such as the monitor 12, may determine a change in theblood pressure signal over a period of time. At step 86, a PPG signalmay also be received by the monitor 12. In certain embodiments, the PPGsignal may be obtained by the one or more sensors 14. At step 88, themonitor 12 may determine the change in the skew metric of the PPG overthe period of time.

As discussed above, the skew metric trends with blood pressure. Thus, itis expected that positive changes in the skew metric will attendpositive changes in the blood pressure signal. Similarly, it is expectedthat negative changes in the skew metric will attend negative changes inthe blood pressure signal. Thus, at step 90, the monitor 12 may beconfigured to correlate the change in the skew metric and the change inblood pressure signal over the period of time. At step 92, the monitor12 may be configured to determine a confidence level of the bloodpressure signal based at least in part on the correlation between thechange in the skew metric and the change in the blood pressure signalover the period of time.

FIG. 5 is a process flow diagram illustrating another method formonitoring blood pressure, in accordance with an embodiment. The methodis generally indicated by reference number 100. As in FIG. 4, the method100 also begins with receiving a blood pressure signal at step 102. Atstep 104, the monitor 12, may determine a change in the blood pressuresignal over a period of time. At step 106, a PPG signal may also bereceived by the monitor 12, and at step 108, the monitor 12 maydetermine a change in the skew metric over the period of time at step108.

Block 110 generally relates to one technique for correlating the changein the blood pressure signal to the change in the skew metric. As shownat step 110, correlating the change in the blood pressure signal withthe change in the skew metric may include determining whether the bloodpressure signal and the skew metric change in the same direction (e.g.,trend in the same direction, positively correlate, both increase, orboth decrease, etc.) over the period of time. As indicated above, theblood pressure signal and the skew metric are expected to change in thesame direction if the monitoring system is functioning properly and ifthe blood pressure signal accurately reflects the patient's bloodpressure.

In certain embodiments, if the blood pressure signal and the skew metricdo not change in the same direction, a low confidence level may bedetermined, as in step 112. The low confidence level may indicate thatthe blood pressure signal is not reliable or that the blood pressuresignal may not accurately reflect the condition of the patient. Forexample, the patient may be experiencing changes in vasotone thatadversely affect the blood pressure signal or the sensor 14 may not beaccurately positioned on the patient. Additionally, certain suitableactions may be trigged when the low confidence level is determined. Forexample, in some embodiments, the blood pressure signal may be discarded(e.g., blood pressure data from the period of time is not used) and/orthe blood pressure signal (or parameters related to the blood pressure)may not be provided to the user or the caregiver. In some embodiments,an indication of the low confidence level may be provided to the user orthe caregiver, along with the blood pressure signal. The indication ofthe low confidence level may include a visual or audible indicator, suchas a message or signal on the display of the monitor 12 or an audiblealarm, for example. Furthermore, in certain embodiments, the monitor 12may be configured to initiate another blood pressure measurement or mayprompt the user to initiate another blood pressure measurement if thelow confidence level is determined.

However, as shown in FIG. 5, if the blood pressure signal and the skewmetric are determined to change in the same direction at step 110, themethod 100 may proceed to step 114. Step 114 also generally relates toone technique for correlating the change in the blood pressure signal tothe change in the skew metric. As shown in step 114, correlating thechange in the blood pressure signal with the change in the skew metricmay include determining a difference between the change in the bloodpressure signal and the change in the skew metric, and comparing thedifference to one or more predetermined (e.g., predefined, programmed,stored, user defined, etc.) tolerance ranges. In addition to trendingwith the blood pressure signal, the skew metric and the blood pressuresignal are also expected to change in a similar manner (e.g., similarpercent change) over the period of time if the system is functioningproperly and if the blood pressure signal accurately reflects thepatient's blood pressure. Thus, gradations of confidence (e.g., multipleconfidence levels) may be determined based on the degree with which theskew metric and the blood pressure signal correlated over the period oftime.

Thus, in some embodiments, the system may determine a high confidencelevel if the difference is within a first predetermined tolerance range,as shown in step 116. The first predetermined tolerance range may bereasonably narrow to ensure that only reasonably reliable blood pressuresignals are determined at the high confidence level. For example, ifthere is a difference of less than 1% (e.g., between 0 and 1%), 2%, 3%,4%, 5%, 6%, 7%, 8%, 9%, 10% or any suitable amount, between theamplitude changes of the two signals, then the high confidence level maybe assigned. The high confidence level may indicate that the bloodpressure signal is reliable or that the blood pressure signal isexpected to accurately reflect the condition of the patient. Certainsuitable actions may be triggered if a high confidence level isdetermined. In some embodiments, the blood pressure signal may beprovided to the user or the caregiver. The blood pressure signal may beprovided on the display 24 of the monitor 12, for example. Additionally,in some embodiments, an indication of the high confidence level may beprovided to the caregiver or the user. The indication may be a visual oran audible indicator, such as a message or a signal provided via thedisplay 14 of the monitor 12.

In some embodiments, the system may determine a second confidence level,such as an acceptable or medium confidence level, if the difference iswithin a second predetermined tolerance range, as shown in step 118. Thesecond predetermined tolerance range should be reasonably narrow so asto ensure that only acceptably reliable blood pressure signals aredetermined to have the acceptable confidence level. For example, if thedifference between the change in the skew metric and the change in theblood pressure signal over the period of time is within about 5-10%,5-10%, 10-20%, 15-20%, or within any suitable range, then the system maydetermine an acceptable confidence level. The second predeterminedtolerance range may depend in part on the first predetermined tolerancerange and may be generally larger than the first predetermined range(e.g., may encompass relatively larger differences between the change inthe blood pressure signal and the change in the skew metric). Theacceptable confidence level may indicate that the blood pressure signalis acceptably reliable or that the blood pressure signal may acceptablyreflect the condition of the patient.

Additionally, certain suitable actions may be triggered if an acceptableconfidence level is determined. For example, in some embodiments, theblood pressure signal may be provided to the user or the caregiver.However, in certain embodiments, the blood pressure signal may bediscarded and/or may not be displayed. In some embodiments, the user mayprovide a user input to control whether the blood pressure signal is tobe provided in the event that an acceptable confidence level isdetermined. For example, prior to a monitoring session, the user mayprovide an input based on the user's preferences, the patient'scondition, or the like. In some embodiments, an indication of theacceptable confidence level may be provided to the caregiver or theuser. The indication may be a visual or audible indicator, such as amessage or signal (e.g., an alarm) provided via the display 16 of themonitor 12. Additionally, the acceptable confidence may be utilized inalarming systems to reduce the incidence of false alarms and/or toprovide more information related to alarms, as discussed above.

As shown in step 120 of FIG. 5, the system may determine a lowconfidence level if the blood pressure signal and the skew metric changein the same direction (as determined at step 110), but the differencebetween the change in the blood pressure signal and the change in theskew metric is outside of the predetermined tolerance ranges (e.g., thefirst and the second predetermined tolerance ranges, or other suitablepredetermined tolerance range). Such a method facilitates identificationof unreliable blood pressure signals if the change in the skew metricdoes not trend with the change in the blood pressure signal in a similarmanner or degree. A large difference between the change in the skewmetric and the change in the blood pressure signal over the period oftime may indicate that the blood pressure signal is not accurate orreliable. By way of example, the monitor 12 may be configured todetermine the high confidence level if the difference between the changein the blood pressure signal and the change in the skew metric over theperiod of time is less than 5%, an acceptable confidence level if thedifference is more than 5% but less than 20%, and a low confidence levelif the difference is greater than 20%. Thus, if the blood pressuresignal increases by 30% over the period of time, but the skew metriconly increases by 5%, or, for example, if the blood pressure signalincreases 30% more than the skew metric increases, then a low confidencelevel may be determined in step 120. In such cases, certain suitableactions may be triggered by the determination of the low confidencelevel, as set forth above.

It should be understood that the various predetermined tolerance rangesprovided above are examples only and are not intended to be limiting.Indeed, any suitable tolerance ranges and any number of tolerance ranges(e.g., 2, 3, 4, 5, 6, 7, 8, or more tolerance ranges) may be utilized toestablish various confidence levels. In such cases, suitablenotifications may be provided and appropriate actions may be triggeredbased on the determined confidence level, including the actions setforth above. Additionally, the predetermined tolerance ranges may beprogrammed into the sensor 14 and/or the monitor 12 at manufacture, oralternatively, the predetermined tolerance ranges may be programmed orselected by the user. For example, the user may set predeterminedtolerance ranges based on the user's preferences, the patient'scondition, or the like. The user may change the tolerance ranges overthe course of monitoring a patient, based on the patient's history orcondition. In an embodiment, the sensor 14 and/or monitor 12 may beprogrammed to automatically change the tolerance ranges based on certainidentified patient conditions or data patterns.

As noted above, it should be understood that the steps of FIG. 4 andFIG. 5 may be carried out in any suitable order or sequence and thesteps of the methods may be adapted and adjusted to accommodate systemand patient requirements. For example, in some embodiments, the bloodpressure signal and the PPG signal may be received and/or processedsimultaneously (e.g., the change in blood pressure and the change in theskew metric may be determined simultaneously). Additionally, in themethod 100 of FIG. 5, the difference between the change in the bloodpressure signal and the change in the skew metric (step 114) may bedetermined prior to determining whether the blood pressure and the skewmetric change in the same direction (step 110), or vice versa. In someembodiments, certain steps shown in FIG. 5 may be omitted to increaseprocessing speed or to provide flexibility in the method. For example,the method may not include determining the difference, but may determinea low confidence level (step 112) if the blood pressure signal and theskew metric change in the opposite direction (step 110), and a highconfidence level if the blood pressure signal and the skew metric changein the same direction.

Additionally, the determination of a confidence level in accordance withFIG. 4 and FIG. 5 may provide additional utility in certain bloodpressure monitoring systems. Some blood pressure monitoring systems maybe configured to provide an alarm when large or clinically significantchanges in blood pressure are detected within a period of time or whenthe blood pressure signal is outside of a certain range. For example,the system may be configured to provide an alarm if the blood pressuresignal indicates that the patient's blood pressure has changed by aclinically significant amount (e.g., about 20 mmHg/minute, in someembodiments). In such systems, the determination of the confidence levelmay be utilized to reduce the incidence of false alarms and/or tovalidate the alarm. For example, if the low confidence level isdetermined, the alarm may be disabled and the blood pressure monitoringsystem may not output or may delay the alarm, thus reducing theincidence of false alarms. The determination of the high confidence maybe utilized to validate alarms related to blood pressure measurements(e.g., clinically high blood pressure) and to improve the accuracy andreliability of such alarming methods in blood pressure monitoring. Forexample, the blood pressure monitoring system may be configured to onlyoutput the alarm if the signal is associated with a high or acceptableconfidence level. In some embodiments, the system may provide anindication of the confidence level along with the alarm.

FIG. 6 is an example of an implementation of a method for monitoringblood pressure, in accordance with an embodiment of the presentdisclosure. The method is generally indicated by reference number 130.As shown in this implementation, a blood pressure signal is received atstep 132 and a percentage change in the blood pressure signal over aperiod of time is determined at step 134. In this example, the bloodpressure signal increases by about 10% over the period of time, althoughit should be understood that in practice the blood pressure signal mayincrease or decrease by any percentage (e.g., 5%, 15%, 20%, 25%, etc.).At step 136, a PPG signal is received, and a change in a skew metricover the period of time is determined at step 138. At step 140, thesystem may determine whether the skew metric changes in the samedirection as the blood pressure signal. For example, where the bloodpressure signal increased by 10%, the system determines if the skewmetric also increased. If the skew metric does not change in the samedirection as the blood pressure signal, then a low confidence level maybe determined and appropriate actions may be triggered at step 142. Insome embodiments, if the skew metric changes in the same direction asthe blood pressure signal, the system may then determine whether theskew metric changes by approximately the same percentage at step 144.For example, the system may determine if the skew metric also increasedby about 10%. If the skew metric increases by approximately the samepercentage (for example, also increased by 10%), or within a tolerancerange of that percentage (for example, plus or minus about 2 percentagepoints, or within 8 to 12%), then the system may determine a highconfidence level at step 146, and appropriate actions may be triggered.If the change in the skew metric is outside of the first predeterminedtolerance of the percentage change in blood pressure (and, optionally,within a second tolerance range), then the system may determine anacceptable confidence level of the blood pressure signal at step 148,and appropriate actions may be triggered. For example, in the exampleprovided, if the change in the skew metric is not approximately 10%, thesystem may determine the acceptable confidence level. Additionally, ifthe change in the skew metric is outside of the second tolerance of thechange in blood pressure, then a low confidence level may be indicated.As indicated above, the steps of the method may be carried out in anysuitable sequence, and any suitable tolerance ranges and any number oftolerance ranges may be utilized to determine various confidence levels.

In some embodiments, certain steps of the methods set forth in FIGS. 4-6may not be carried out unless or until the system detects a bloodpressure signal that changes by a certain amount or percentage over aperiod of time. For example, in the example of FIG. 6, the system may beconfigured to initiate the determination of the skew metric and/or theconfidence level only if the blood pressure signal changes by 10% ormore over a period of time. Thus, the blood pressure may be continuouslymonitored without determining confidence levels of the signal, and theskew metric and the confidence level are only determined if the bloodpressure signal changes by a certain threshold amount, or if apredetermined time interval has passed. In some embodiments, the systemmay be configured to switch from periodically (e.g., at predefinedintervals) validating the blood pressure signal based on the skew metricto continuously evaluating the reliability of the blood pressure signalbased on the skew metric if large changes in the blood pressure signalare detected. Such systems may reduce processing steps and improveprocessing speed, but may still provide a check or indication ofconfidence levels during periods of large changes in the blood pressuresignal.

FIG. 7 is a process flow diagram illustrating another method formonitoring blood pressure, in accordance with one embodiment. Asdiscussed above, in some embodiments, blood pressure may becontinuously, non-invasively monitored based on the DPTT. As notedabove, the DPTT may be obtained by using multiple sensors 14 located atmultiple sites on a patient. The multiple PPG signals obtained from themultiple sensors 14 may be compared against each other to determine DPTTand from that estimate the patient's blood pressure. However, the DPTTmay be adversely affected by various physiological events andconditions, such as changes in vasotone. Thus, the DPTT may not alwaysprovide accurate estimations of the blood pressure of the patient. Insome embodiments, it may be desirable to determine a confidence levelrelated to the DPTT, which may provide an indication of whether the DPTTcan be reliably used to monitor blood pressure.

The DPTT trends negatively with blood pressure, and thus, the DPTT isexpected to trend negatively with the skew metric. This inversecorrelation or relationship may be utilized by the system 10 todetermine whether the DPTT can be reliably used to monitor bloodpressure. The determination may be made via similar methods as set forthin FIGS. 4-7, although the methods should be adapted to account for thefact that the DPTT is expected to negatively correlate with the skewmetric. Determining the confidence level in the DPTT may eliminateunnecessary processing steps and improve the processing speed of certainblood pressure monitoring systems. For example, if the expectedcorrelation is not present between the DPTT and the skew metric, theDPTT data may be discarded without unnecessarily calculating aninaccurate or unreliable blood pressure signal.

As illustrated in method 150 of FIG. 7, multiple PPG signals arereceived at step 152. At step 154, the change in the DPTT is determinedover a period of time. At step 156, the change in the skew metric isalso determined over the period of time based on at least one of thereceived PPG signals. At step 158, the monitor 12 may determine whetherthere is an expected correlation between the change in the DPTT and thechange in the skew metric. As indicated above, it is expected thatdecreases in the skew metric will attend increases in the DPTT.Similarly, it is expected that increases in the skew metric will attenddecreases in the DPTT. If the expected correlation is present, thesystem may determine a high confidence level in the DPTT at step 160,and certain suitable actions may be triggered. For example, in someembodiments, the monitor 12 may proceed to calculate blood pressurebased on the DPTT. In certain embodiments, the monitor 12 may provide anindication of the high confidence level to the user or the caregiver, asset forth above. However, if the expected correlation is not present,the system may determine a low confidence level in the DPTT at step 162,and certain suitable actions may be triggered. For example, in someembodiments, the DPTT data may be discarded and/or the monitor 12 maynot proceed to derive the blood pressure from the DPTT data. In certainembodiments, an indication of the low confidence level may be provided,as set forth above. It should be understood that the method 150 may beadapted to determine gradations in confidence levels as described abovewith respect to FIG. 5. For example, in some embodiments, the method 150may be adapted to determine whether the DPTT and the skew metric changein the same direction and/or to determine whether the difference betweenthe change in the DPTT and the change in the skew metric is within oneor more predetermined tolerance ranges as described above with respectto FIG. 5.

As noted above, any of a variety of other suitable metrics may be usedto determine the confidence level of a blood pressure signal inaccordance with the techniques disclosed herein. For example, in someembodiments, the metric may be any suitable pulse shape metric. Thepulse shape metric may be a center of area, a rotational moment, a mean,a median, a standard deviation, kurtosis, a pulse wave area, peak topeak amplitude, a natural frequency, or a damping factor. Additionally,in some embodiments, an indirect pulse shape metric may be utilized todetermine the confidence level of the blood pressure signal. By way ofexample, a first Gaussian function may be fitted to a first peak of theblood pressure signal, and successive Gaussian functions may beiteratively fitted to subsequent residuals. A position and an amplitudeof these fitted Gaussian functions may provide an indirect metric thatdescribes the shape of the blood pressure signal.

As noted above, the metrics may be determined with respect to the rawPPG signal or a derivative of the PPG signal (e.g., a first derivative,a second derivative, etc.). In certain embodiments, the metrics may bedetermined with respect to a whole pulse or only with respect to anupstroke portion (e.g., systolic) portion of the pulse. Each of themetrics may be expected to correlate with the blood pressure signal in aparticular manner, and the relationship between the blood pressuresignal and the pulse shape metric over a period of time may be used todetermine a confidence level of the blood pressure signal. Additionally,any combination of two or more different pulse shape metrics may be usedto determine a confidence level of the blood pressure signal. In someembodiments, such metrics or indirect metrics (e.g., a center of area, arotational moment, a mean, a median, a standard deviation, kurtosis, apulse wave area, peak to peak amplitude, a natural frequency, damping,Gaussian fit, etc.) may be related to (e.g., may be indicative of) theelasticity of the arterial system or to the change in the elasticity ofthe arterial system, which in turn may be correlated to the bloodpressure or to the change in the blood pressure over a period of time bythe Moens-Korteweg-Hughes equation. Thus, the above-disclosed methodsfor determining a confidence level of a blood pressure signal may beadapted to determine the confidence level based on various other metricsset forth herein. For example, if the expected correlation betweenchanges in the damping factor and changes in the blood pressure signalare not observed, the system 10 may determine that the blood pressuresignal has a low confidence level. In yet other embodiments, if thechange in the damping factor varies by more than a threshold percentagefrom the change in the blood pressure signal obtained via the bloodpressure cuff or derived from the DPTT, the system 10 may determine thatthe blood pressure signal has a low confidence level as described abovewith respect to FIGS. 4-7.

FIG. 8 is a process flow diagram illustrating an alternative method formonitoring blood pressure of a patient using the skew of the firstderivative of the PPG signal. In some embodiments, blood pressure may becontinuously monitored using only a single PPG signal from a singlesensor 14. As illustrated in the graphs of FIG. 3, the skew metrictrends with pulse pressure. As discussed above, pulse pressure is thedifference between systolic pressure and diastolic pressure, and isrelated to a patient's blood pressure. Blood pressure is related to theamount of pressure exerted by the blood against the walls of a bloodvessel, and systolic pressure is generally a pressure that is exertedwhen the patient's heart contracts (e.g., near the end of a strokeoutput of a left ventricle of the patient's heart), while diastolicpressure is generally a pressure that is exerted when the patient'sheart relaxes (e.g., during ventricular diastole). Thus, monitoring thePPG signal and calculating the skew metric over a period of time canprovide an indication of changes in pulse pressure, and thus, changes inblood pressure. In some embodiments, the skew metric may be displayed ona display of the monitor 12 to provide additional data related tochanges in blood pressure. In certain embodiments, a baseline bloodpressure may be determined, and the skew metric may be utilized toprovide an indication of whether the blood pressure is increasing ordecreasing over time. In some embodiments, certain actions may betriggered based on whether the skew metric changes by a certain amount(e.g., more than a threshold percentage over a period of time). Forexample, if the skew metric changes significantly (e.g., by 10%, 15%,20%, 25%, or more), the system may be configured to take a bloodpressure reading, to provide an alarm, and/or to provide an indicationthat the blood pressure is changing. As noted above, the skew metric isnot influenced by changes in vasotone, and thus, in some circumstancesmay provide an accurate depiction of relative changes in pulse pressureand/or blood pressure.

Accordingly, as shown in FIG. 8, the skew metric may be appropriatelyscaled to generate blood pressure measurements or parameters related toblood pressure. The method 160 may begin when a PPG signal is receivedin step 162. At step 164, the skew of the first derivative of the PPGsignal over time is determined. As shown in step 166, the skew of thefirst derivative may be appropriately scaled to determine bloodpressure. For example, a linear scale factor may be applied toappropriately scale the skew to provide a blood pressure measurement. Insome embodiments, the scale factor may be preprogrammed into a memory(e.g., a memory of the monitor 12 or sensor 14) based on empirical data.In certain embodiments, the scale factor may be determined for aparticular patient based on a baseline measurement of the difference inpulse pressure measurements and the difference in skew measurements at aparticular time. Then, the blood pressure signal or a parameter relatedto the blood pressure may be provided to the caregiver or user in step168. For example, the blood pressure signal derived from the skew metricmay be provided on the display of the monitor 12. Alternatively or inaddition, a response may be triggered based on the blood pressure signalor changes in the blood pressure signal as shown in step 170. In someembodiments, the response may be triggered if the skew and/or the bloodpressure changes by a predetermined amount or percentage over a periodof time (e.g., by 5%, 10%, 15%, or more over 1 minute, 2 minutes, orlonger). For example, in some embodiments, the monitor 12 may beconfigured to automatically initiate a new blood pressure measurement.In some embodiments, the monitor 12 may be configured to provide amessage or indication relating to blood pressure may be provided on thedisplay 24 of the monitor 12 to trigger a suitable response. In someembodiments, an indication that a new blood pressure measurement isneeded, may be provided on the display 24 of the monitor 12. In someembodiments, a message instructing the user to change the patient'sposture, adjust drug delivery, or initiate another suitable interventionmay be provided on the display 24 of the monitor 12. Such methods andsystems may advantageously facilitate blood pressure monitoring withonly a single PPG sensor, thus facilitating monitoring with lesshardware and/or different processing steps. Furthermore, such methodsand systems may enable the determination of blood pressure withoutadverse affects due to changes in vasotone.

As noted above, various other metrics may be related to the elasticityof the patient's arterial system and/or to the patient's blood pressure.Thus, such metrics may also be utilized by the monitor 12 tocontinuously monitor the patient's blood pressure and/or pulse pressureusing only a signal sensor 14. For example, in some embodiments, themetric may be any suitable pulse shape metric, such as a center of area,a rotational moment, a mean, a median, a standard deviation, kurtosis, apulse wave area, peak to peak amplitude, a mean path ratio, notchpositioning, a natural frequency (or an inverse thereof), or a dampingfactor. As noted above, in some embodiments, an indirect pulse shapemetric, such as a Gaussian fit, may be related to the elasticity of thearterial system and/or to the blood pressure signal. Accordingly,changes in such metrics may be monitored and appropriately scaled orprocessed to provide an indication of the blood pressure signal, andthus, the technique set forth in FIG. 8 may be adapted to utilize suchother metrics to monitor the patient's blood pressure. In someembodiments, the metrics may be correlated to the elasticity of thearterial system or to the change in the elasticity of the arterialsystem, which in turn may be input into the Moens-Korteweg-Hughesequation to determine or to monitor the patient's blood pressure.

Additionally, in some embodiments, changes in such metrics may bemonitored and utilized to determine whether significant (e.g.,clinically significant) changes in blood pressure occur. By way ofexample, a reference blood pressure measurement may be obtained and/ormonitored via any suitable technique (e.g., via a blood pressure cuff,derived from a DPTT, etc.). In some embodiments, a reference calibration(e.g., a scaling factor) between one or more of the metrics describedherein and the blood pressure signal may be determined. The referencecalibration may be determined through any suitable technique, such as bymonitoring a patient's blood pressure and/or one or more of the metricsover a range of blood pressures as the patient raises or lowers theirhand or otherwise changes internal transmural pressures. Changes in oneor more of the metrics may be monitored over a period of time, andcalibrated (e.g., calibrated via the reference calibration and/orappropriately scaled) to monitor changes in blood pressure. In certainembodiments, the one or more metrics may be monitored and large changes(e.g., changes greater than a predetermined threshold, such as aboutmore than 3%, 5%, 10%, 15%, 20%, 25%, or more, over a period of time,such as about 0.5, 1, 2, 3, or more minutes) may trigger an alarm orprompt other actions, such as prompting an operator to obtain an updatedblood pressure signal via the blood pressure cuff, for example.

In some embodiments, the system 10 may be configured to utilize variouscombinations of the methods for blood pressure monitoring providedherein. FIG. 9 is a process flow diagram illustrating a method formonitoring blood pressure of a patient using multiple differentmonitoring techniques. As shown in step 80, the system 10 may monitorblood pressure based on the DPTT or via another suitable blood pressuremonitoring device. For example, the system 10 may monitor the patient'sblood pressure in accordance with method 80 of FIG. 4. In step 82, thesystem 10 may determine a confidence level of the blood pressure signalvia any suitable technique or using any suitable metric. If it isdetermined that the blood pressure signal has a low confidence level atstep 84, the system 10 may adjust (e.g., switch, change, etc.)monitoring modes and/or may begin to determine the blood pressure basedon the skew metric (e.g., as set for in the method 160 of FIG. 8) so asto avoid the adverse effects due to changes in vasotone. The system 10may temporarily change monitoring techniques for a predetermined amountof time, or the system may change monitoring techniques until promptedor instructed by a user input. In some embodiments, the user orcaregiver may be able to select a preferred monitoring method based onvarious factors, such as the user's preferences, the patient's needs, orbased on whether the patient is being administered medication that maycause changes in vasotone. For example, the patient's blood pressure maybe monitored via method 80, and the user may provide an input to changethe monitoring method to method 160 if certain medications that affectvasotone are being administered to the patient.

While the disclosure may be susceptible to various modifications andalternative forms, specific embodiments have been shown by way ofexample in the drawings and have been described in detail herein.However, it should be understood that the embodiments provided hereinare not intended to be limited to the particular forms disclosed.Rather, the various embodiments may cover all modifications,equivalents, and alternatives falling within the spirit and scope of thedisclosure as defined by the following appended claims. Further, itshould be understood that certain elements of the disclosed embodimentsmay be combined or exchanged with one another.

1.-23. (canceled)
 24. A patient monitor configured to monitor blood pressure, comprising: a memory, comprising machine-readable instructions that, when executed by one or more processors, cause the one or more processors to: receive a plurality of plethysmography signals representative of one or more patient parameters; determine a change in a differential pulse transit time of the plurality of plethysmography signals over a period of time; determine a change in a pulse shape metric of a plethysmography signal of the plurality of plethysmography signals over the period of time; determine a confidence level based on whether there is an expected correlation between the change in the differential pulse transit time and the change in the pulse shape metric over the period of time, wherein the confidence level is determined to be a low confidence level based on a positive correlation between the change in the differential pulse transit time and the change in the pulse shape metric over the period of time; and in response to determining the confidence level is the low confidence level, switching from determining a blood pressure via a first monitoring mode to determining a blood pressure via a second monitoring mode different from the first monitoring mode.
 25. The patient monitor of claim 24, wherein the pulse shape metric is a skew of a first derivative of the plethysmography signal.
 26. The patient monitor of claim 24, wherein the memory comprises instructions that cause the one or more processors to discard blood pressure data determined via the first monitoring mode over the period of time in response to determining the confidence level is the low confidence level.
 27. The patient monitor of claim 24, wherein the confidence level is determined to be a high confidence level based on an inverse correlation between the change in the differential pulse transit time and the change in the pulse shape metric over the period of time.
 28. The patient monitor of claim 27, wherein the instructions cause the one or more processors to provide an indication of the high confidence level via a display.
 29. The patient monitor of claim 24, wherein determining the blood pressure via the first monitoring mode comprises determining the blood pressure based on the differential pulse transit time of the plurality of plethysmography signals over the period of time.
 30. The patient monitor of claim 24, wherein determining the blood pressure via the second monitoring mode comprises determining the blood pressure based on the pulse shape metric of the plethysmography signal.
 31. A method, comprising: receiving, via a processor, a plurality of plethysmography signals representative of one or more patient parameters; generating, via the processor, a blood pressure measurement based on a differential pulse transit time of the plurality of plethysmography signals; determining, via the processor, a change in the differential pulse transit time over a period of time; determining, via the processor, a change in a pulse shape metric of a plethysmography signal of the plurality of plethysmography signals over the period of time; determining, via the processor, a low confidence level associated with the blood pressure measurement based on a positive correlation between the change in the differential pulse transit time and the change in the pulse shape metric over the period of time; and generating, via the processor, the blood pressure measurement based on the pulse shape metric of the plethysmography signal in response to determining the low confidence level.
 32. The method of claim 31, wherein the pulse shape metric is a skew of a first derivative of the plethysmography signal.
 33. The method of claim 31, comprising discarding, via the processor, the blood pressure measurement based on the differential pulse transit time over the period of time in response to determining the low confidence level.
 34. The method of claim 31, comprising providing, via the processor, an indication of the low confidence level via a display.
 35. The method of claim 31, comprising determining, via the processor, a high confidence level associated with the blood pressure measurement based on an inverse correlation between the change in the differential pulse transit time and the change in the pulse shape metric over a different period of time, wherein the blood pressure measurement is generated based on the differential pulse transit time over the different period of time.
 36. The method of claim 31, comprising providing an indication of the blood pressure measurement on a display.
 37. A system for monitoring blood pressure comprising: a plurality of plethysmography sensors configured to obtain respective plethysmography signals; a patient monitor configured to: receive a plurality of plethysmography signals representative of one or more patient parameters; determine a change in a differential pulse transit time of the plurality of plethysmography signals over a period of time; determine a change in a pulse shape metric of a plethysmography signal of the plurality of plethysmography signals over the period of time; determine a high confidence level based on an inverse correlation between the change in the differential pulse transit time and the change in the pulse shape metric over the period of time; and generating a blood pressure measurement based on the differential pulse transit time in response to determining the high confidence level.
 38. The system of claim 37, wherein the pulse shape metric is a skew of a first derivative of the plethysmography signal.
 39. The system of claim 37, wherein the patient monitor is configured to determine a low confidence level based on a positive correlation between the change in the differential pulse transit time and the change in the pulse shape metric over the period of time.
 40. The system of claim 39, wherein the patient monitor is configured to switch from generating the blood pressure measurement based on the differential pulse transit time to generating the blood pressure measurement based on the pulse shape metric of the plethysmography signal in response to determining the low confidence level.
 41. The system of claim 40, wherein generating the blood pressure measurement based on the pulse shape metric comprises generating the blood pressure measurement based on a scaled skew of the first derivative of the plethysmography signal.
 42. The system of claim 39, wherein the patient monitor is configured to discard the blood pressure measurement over the period of time in response to determining the low confidence level.
 43. The system of claim 37, wherein the patient monitor is configured to provide an indication of the blood pressure measurement and the high confidence level to a display. 